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bioRxiv · 10.1101/551671

Graph Signal Processing of Low and High-Order Dynamic Functional Connectivity Networks Using EEG Resting-State for Schizophrenia: A Whole Brain Breakdown

Dimitriadis, S. I.
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Abstract

Conventional static or dynamic functional connectivity graph (FCG/DFCG) referred to as low-order FCG focusing on temporal correlation estimates of the resting-state electroencephalography (rs-EEG) time series between any potential pair of brain areas. A DFCG is first constructed from multichannel recordings by adopting the methodology of sliding-window and a proper functional connectivity estimator. However, low-order FC ignores the high-level inter-relationship of brain areas. Recently, a high-order version of FCG has emerged by estimating the correlations of the time series that describe the fluctuations of the functional strength of every pair of ROIs across experimental time. In the present study, a dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV). We analyzed DFCG profiles of electroencephalographic resting state (eyes-closed) recordings of healthy controls subjects (n=39) and subjects with symptoms of schizophrenia (n=45) in basic frequency bands {{delta},{theta},1,2,{beta}1,{beta}2,{gamma}}. In our analysis, we incorporated both intra and cross-frequency coupling modes. Adopting our recent Dominant Intrinsic Coupling Mode (DICM) model leads to the construction of an integrated DFCG (iDFCG) that encapsulates both the functional strength but also the DICM of every pair of brain areas. Based on the LO - IDFCG, we constructed the HO- IDFCG by adopting the cosine similarity between the time-series derived from the LO-DIFCG. At a second level, we estimated the laplacian transformations of both LO and HO-IDFCG and by calculating the temporal evolution of Synchronizability (Syn), four network metric time series (NMTSSyn) were produced. Following, a machine learning approach based on multi-kernel SVM with the four NMTSSyn used as potential features and appropriate kernels, we succeeded a superior classification accuracy ([~]98%). DICM and flexibility index (FI) achieved a classification with absolute performance (100 %) Schizophrenic subjects demonstrated a hypo-synchronization compared to healthy control group which can be interpreted as a low global synchronization of co-fluctuate functional patterns. Our analytic pathway could be helpful both for the design of reliable biomarkers and also for evaluating non-intervention treatments tailored to schizophrenia. EEG offers a low-cost environment for applied neuroscience and the transfer of research knowledge from neuroimaging labs to daily clinical practice.

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Dimitriadis, S. I.. 2019-02-15. Graph Signal Processing of Low and High-Order Dynamic Functional Connectivity Networks Using EEG Resting-State for Schizophrenia: A Whole Brain Breakdown. https://doi.org/10.1101/551671

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